A machine learning-based framework for user recruitment in continuous mobile crowdsensing

Abstract

Mobile Crowdsensing (MCS) is a sensing paradigm where individuals collectively perform a sensing task using their smart devices. Sensing tasks can be classified as one-time or continuous. In the former, only one-time readings from the devices of the recruited workers are needed. However, in continuous sensing tasks, collecting information continuously during a specific period is required. Due to workers’ mobility, ensuring a satisfactory level of the quality of information (QoI) of the sensing data is challenging since workers may leave the Area of Interest (AoI) before the task is over, causing low area coverage. Current existing recruitment systems for continuous sensing rely on historical mobility traces to recruit the group of workers. However, since workers’ mobility patterns are dynamic in nature, thus, a real-time prediction of their locations in the AoI needs to be considered to ensure that the required value of QoI is achieved. Hence, in this work (1) machine learning is employed to predict users’ location during the sensing period and (2) a novel recruitment system is proposed for continuous sensing tasks. The simulation results, using a real-life trajectories dataset, show the efficacy of the proposed solution when compared to benchmark.